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This paper presents a Multimodal Emotion-Based Attendance Monitoring System that evaluates both physical presence and cognitive engagement using facial recognition, speech analysis, and posture detection. Unlike traditional methods focused solely on presence, our system records attendance only when engagement exceeds a defined threshold. Facial expressions, vocal sentiment, and posture cues are processed in real-time to determine student attentiveness. A pilot study with 15 students demonstrated a 92% accuracy rate—outperforming traditional methods 78% highlighting the advantages of multimodal fusion. The system shows promise for improving engagement insights and enabling timely, data-driven educational interventions. Future work aims at real-time deployment and enhanced engagement scoring.

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Multimodal Emotion-Based Attendance and Engagement Monitoring System

  • 会议论文

  • 发表日期：

    2026年

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  • Gunendra M. Chaturvedi,
  • Kajal Gera,
  • Katyayni Dhiren,
  • Gopal Singh,
  • Akhilesh Verma

摘要

This paper presents a Multimodal Emotion-Based Attendance Monitoring System that evaluates both physical presence and cognitive engagement using facial recognition, speech analysis, and posture detection. Unlike traditional methods focused solely on presence, our system records attendance only when engagement exceeds a defined threshold. Facial expressions, vocal sentiment, and posture cues are processed in real-time to determine student attentiveness. A pilot study with 15 students demonstrated a 92% accuracy rate—outperforming traditional methods 78% highlighting the advantages of multimodal fusion. The system shows promise for improving engagement insights and enabling timely, data-driven educational interventions. Future work aims at real-time deployment and enhanced engagement scoring.